Systems and methods for impervious surface detection and classification

ABSTRACT

Systems and methods are provided for impervious surface mapping of a target geographic area. The impervious surface mapping utilizes four-band imagery data and light detection and ranging (LIDAR) data collected from the target geographic area. The identified impervious surfaces can be attributed to parcels within the target geographic area for purposes of generating bills for storm water runoff to parcel owners.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Ser. No. 17/156,737, entitledSYSTEMS AND METHODS FOR IMPERVIOUS SURFACE DETECTION AND CLASSIFICATION,filed on Jan. 25, 2021, which is a continuation of U.S. Ser. No.15/906,196, now U.S. Pat. No. 10,901,091, entitled SYSTEMS AND METHODSFOR IMPERVIOUS SURFACE DETECTION AND CLASSIFICATION, filed on Feb. 27,2018, the disclosures of which are incorporated herein by reference intheir entirety.

BACKGROUND

Impervious surfaces prevent water from absorbing into the ground andgreatly impact natural processes on land and in bodies of water.Impervious surface is any material, natural or manmade, that preventsthe infiltration of surface water to the underlying strata. Impervioussurfaces can be buildings, roads, sidewalks, parking lots or other pavedsurfaces. Such surfaces increase runoff volume, transport contaminants,and are the leading contributor to non-point source pollution in urbanwatersheds.

As a city grows, so does its amount of impervious surface area. To helpcurb these negative effects to stormwater systems, the EnvironmentalProtection Agency's National Pollutant Discharge Elimination System(NPDES) program requires environmentally conscious stormwater managementpractices. Many municipalities and local government agencies areforming, or have formed, stormwater utilities to address an increasingfiscal burden of meeting many of the water quality regulatoryrequirements. Stormwater utilities can generate bills for propertyowners within the municipality based on the impervious surface areaowned by the property owner. The determination of pervious/impervioussurface area contained within each parcel of the municipality, however,can be a significant task for such stormwater utilities. Furthermore,with continuing development, both in existing developed areas and newdevelopments, maintaining a complete and up-to-date parcel map, alongwith the determination of pervious/impervious surfaces, is a continuingchallenge for many communities.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be more readily understood from a detaileddescription of some example embodiments taken in conjunction with thefollowing figures:

FIG. 1 depicts a system flow diagram of an example impervious surfacemapping computing system.

FIG. 2 depicts an example impervious surface mapping process utilizingaerially collected four-band imagery and light detection and ranging(LIDAR) data.

FIG. 3 depicts another example impervious surface mapping process.

FIG. 4 schematically depicts an example target geographic zone beinggrouped into geographic zones for determination of unique classificationparameters for each geographic zone.

FIG. 5 schematically depicts an example target geographic zone havingimpervious surfaces.

FIG. 6 schematically depicts the identification of impervious surfacesof the example target geographic zone of FIG. 5 and example processingrules for determining the same.

FIG. 7 schematically depicts an example rule for determining theimpervious surfaces of the example target geographic zone of FIG. 5.

FIG. 8 schematically depicts the correlation of determined impervioussurfaces to parcels.

DETAILED DESCRIPTION

Various non-limiting embodiments of the present disclosure will now bedescribed to provide an overall understanding of the principles of thestructure, function, and use of the impervious surface mapping systemsand methods disclosed herein. One or more examples of these non-limitingembodiments are illustrated in the accompanying drawings. Those ofordinary skill in the art will understand that systems and methodsspecifically described herein and illustrated in the accompanyingdrawings are non-limiting embodiments. The features illustrated ordescribed in connection with one non-limiting embodiment may be combinedwith the features of other non-limiting embodiments. Such modificationsand variations are intended to be included within the scope of thepresent disclosure.

Reference throughout the specification to “various embodiments,” “someembodiments,” “one embodiment,” “some example embodiments,” “one exampleembodiment,” or “an embodiment” means that a particular feature,structure, or characteristic described in connection with any embodimentis included in at least one embodiment. Thus, appearances of the phrases“in various embodiments,” “in some embodiments,” “in one embodiment,”“some example embodiments,” “one example embodiment, or “in anembodiment” in places throughout the specification are not necessarilyall referring to the same embodiment. Furthermore, the particularfeatures, structures, or characteristics may be combined in any suitablemanner in one or more embodiments.

Throughout this disclosure, references to components or modulesgenerally refer to items that logically can be grouped together toperform a function or group of related functions. Like referencenumerals are generally intended to refer to the same or similarcomponents. Components and modules can be implemented in software,hardware, or a combination of software and hardware. The term softwareis used expansively to include not only executable code, but also datastructures, data stores, and computing instructions in any electronicformat, firmware, and embedded software. The terms information and dataare used expansively and can include a wide variety of electronicinformation, including but not limited to machine-executable ormachine-interpretable instructions; content such as text, video data,and audio data, among others; and various codes or flags. The termsinformation, data, and content are sometimes used interchangeably whenpermitted by context.

The examples discussed herein are examples only and are provided toassist in the explanation of the systems and methods described herein.None of the features or components shown in the drawings or discussedbelow should be taken as mandatory for any specific implementation ofany of these systems and methods unless specifically designated asmandatory. For ease of reading and clarity, certain components, modules,or methods may be described solely in connection with a specific figure.Any failure to specifically describe a combination or sub-combination ofcomponents should not be understood as an indication that anycombination or sub-combination is not possible. Also, for any methodsdescribed, regardless of whether the method is described in conjunctionwith a flow diagram, it should be understood that unless otherwisespecified or required by context, any explicit or implicit ordering ofsteps performed in the execution of a method does not imply that thosesteps must be performed in the order presented but instead may beperformed in a different order or in parallel.

As described in more detail below, the systems and methods describedherein can utilize aerially collected geographic surface data toidentify and quantify impervious surfaces within a target geographicarea. In accordance with various embodiments, the aerially collectedgeographic surface data can comprises four-band imagery and lightdetection and ranging (LIDAR) data. Using this data, object-based imageanalysis (OBIA) can allow for precise feature extraction. For example,four-band imagery provides a color infrared (CIR) band that can makevegetation present as red and paved surfaces present as grey orotherwise a dark color. LIDAR data collected from the target geographicarea, which uses pulsed light, can provide for elevation modeling.Additionally, as paved surfaces absorb the light pulses and trees andgrass reflect the pulses, the strength of the returned pulses can alsoplay a role in the identification of objects within a target geographicarea.

As described in more detail below, certain surfaces, such as publicroadways and municipal sidewalks, can be excluded from the results in anautomated fashion. Impervious surfaces can ultimately be linked to aparticular parcel within the target geographic area for billingpurposes. In some embodiments, impervious surfaces that are notphysically located within the boundaries of a particular parcel canstill be associated with that parcel for billing purposes.

FIG. 1 depicts a system flow diagram of an example impervious surfacemapping computing system 100 in accordance with one non-limitingembodiment. The impervious surface mapping computing system 100 can beused, for instance, to identify and quantify impervious surfaces withina particular geographic area. The system flow diagram includes threeexample processing phases, a mapping preparation phase 162, a datacollection phase 164, and a data processing phase 166, each of which isdescribed in more detail below.

The impervious surface mapping computing system 100 can be providedusing any suitable processor-based device or system, such as a personalcomputer, laptop, server, mainframe, or a collection (e.g., network) ofmultiple computers, for example. The impervious surface mappingcomputing system 100 can include one or more processors (e.g., 108) andone or more computer memory units (e.g., 110). For convenience, only oneprocessor 108 and only one memory unit 110 are shown in FIG. 1. Theprocessor 108 can execute software instructions stored on the memoryunit 110. The processor 108 can be implemented as an integrated circuit(IC) having one or multiple cores. The memory unit 110 can includevolatile and/or non-volatile memory units. Volatile memory units caninclude random access memory (RAM), for example. Non-volatile memoryunits can include read only memory (ROM), for example, as well asmechanical non-volatile memory systems, such as, for example, a harddisk drive, an optical disk drive, etc. The RAM and/or ROM memory unitscan be implemented as discrete memory ICs, for example.

The memory unit 110 can store executable software and data, such thatwhen the processor 108 of the impervious surface mapping computingsystem 100 executes the software, the processor 108 can be caused toperform the various operations of the impervious surface mappingcomputing system 100, such as receive information from computer devicesor other sources, receive aerially collected geographic surface data,and process the aerially collected geographic surface data to determinea derived area of impervious surface, as discussed in more detail below.Data used by the impervious surface mapping computing system 100 can befrom various sources, such as from databases 116, third party sources,and/or other types of electronic data stores. The data stored in thedatabases 116 can be stored in a non-volatile computer memory, such as ahard disk drive, a read only memory (e.g., a ROM IC), or other types ofnon-volatile memory. Also, the data associated with the databases 116can be stored on a remote electronic computer system, for example.

As shown in FIG. 1, in some embodiments, the impervious surface mappingcomputing system 100 can include several computer servers. For example,the impervious surface mapping computing system 100 can include one ormore web servers (e.g., 112) and application servers (e.g., 114). Forconvenience, only one web server 112 and one application server 114 areshown in FIG. 1, although it should be recognized that this disclosureis not so limited. In accordance with various embodiments, the webserver 112 can provide a graphical web user interface through whichusers of the system can interact with the impervious surface mappingcomputing system 100. The web server 112 can accept requests, such asHTTP requests, from clients, and serve the clients responses, such asHTTP responses, along with optional data content, such as web pages(e.g., HTML documents) and linked objects (such as images, etc.).

In some embodiments, the application server 114 can provide a userinterface for users who do not communicate with the impervious surfacemapping computing system 100 using a web browser. Such users can have,for example, special software installed on a computing device thatallows them to communicate with the application server 114 via acommunications network. Such software can be downloaded, for example,from the impervious surface mapping computing system 100, or othersoftware application providers.

Although FIG. 1 depicts a limited number of elements for purposes ofillustration, it can be appreciated that the impervious surface mappingcomputing system 100 can include more or fewer elements as well as othertypes of elements in accordance with the described embodiments. Elementsof the impervious surface mapping computing system 100 can includephysical or logical entities for communicating information implementedas hardware components (e.g., computing devices, processors, logicdevices), executable computer program instructions (e.g., firmware,software) to be executed by various hardware components, or acombination thereof, as desired for a given set of design parameters orperformance constraints.

Interactions with the impervious surface mapping computing system 100for a particular target geographic can generally occur in phases.Referring first to the mapping preparation phase 162, the impervioussurface mapping computing system 100 can receive or otherwise beprovided with information to prepare for the impervious surface mappingprocess of a particular target geographic area 130. By way of example, amunicipal parameter set 102 can be provided to the impervious surfacemapping computing system 100 that can comprise a plurality of rules 104for determining impervious surfaces. Example rules 104 can, forinstance, define certain surface types (i.e., gravel, packed dirt, etc.)that qualify as impervious surfaces within that jurisdiction.Additionally or alternatively, the rules 104 can define which impervioussurfaces are to be included in the derived area of impervious surfaceareas, even if such impervious surfaces are located outside a parcel,such as driveway aprons. Other example rules 104 can define thejurisdiction's treatment of pools and decks, among other types of rules.

Additional information, such as parcel data 106, can be provided to theimpervious surface mapping computing system 100 during the mappingpreparation phase 162. The parcel data 106 can include, for instance,GIS parcel boundary data for each parcel within the target geographicarea 130. Additionally, the parcel data 106 can indicate the owner ofthe parcel by way of a tax identifier, as may be used for property taxassessment, for example. In accordance with some implementations,utility data 126 can also be provided to the impervious surface mappingcomputing system 100. The utility data 126 can include, for example,billing data for a stormwater utility for addresses within themunicipality. This utility data 126 can be used to associate geocodedaddresses within the target geographic area 130 with unique utilitycustomer identifiers. As described in more detail below (see e.g., FIG.8), using the parcel data 106 and the utility data 126, the impervioussurface mapping computing system 100 can correlate the taxable ID of aparcel (which can be maintained by a county) and correlate it to acustomer number of a stormwater utility (which can be maintained by amunicipality) based on the addresses provide by the municipalityutilizing a geocoding process.

During the data collection phase 164, aerially collected geographicsurface data 118 is collected. The data collection phase 164 can occurprior to the mapping preparation phase 162 or subsequent to the mappingpreparation phase 162. The aerially collected geographic surface data118 can be gathered by an aerial data acquisition unit 120 that ismounted in an aircraft that flies over the target geographic area 130.In other embodiments, the aerial data acquisition unit 120 is deployedon a satellite. The aerial data acquisition unit 120 provides forgeospatial mapping and can include a variety of onboard sensors, such asan image sensor 122 and a LIDAR sensor 124.

In accordance with one embodiment, the image sensor 122 provides fourbands of the electromagnetic spectrum, including red (610-660 nm), green(530-580 nm), blue (435-495 nm) and near-infrared (840-1,000 nm). TheLIDAR sensor 124 can be mounted on the underside of the aircraft and cantransmit pulses of a narrow laser beam towards the target geographicarea 130. A receiver affixed to the aircraft receives reflections of thepulses as they bounce off the ground surface and objects on the groundand then back to the aircraft. Many LIDAR systems use a scanning mirrorto generate a swath of light pulses. Swath width depends on the mirror'sangle of oscillation, and the ground-point density depends on factorssuch as aircraft speed and mirror oscillation rate. Each LIDAR laserreflection provides a single point on the ground. Elevations can bedetermined by computing the amount of time it takes light to leave anairplane, travel to the ground and return to the sensor. The intensityof the energy collected by the LIDAR sensor 124 can further assist theimpervious surface mapping computing system 100 with classification ofthe ground phenomenon.

In accordance with the present disclosure, the four-band imagery dataand LIDAR data can be co-collected while the aircraft equipment with theaerial data acquisition unit 120 flies over a target geographic area 130and uploaded to the impervious surface mapping computing system 100. Thetarget geographic area 130 can include, for example, the municipality towhich the municipal parameter set 102, the parcel data 106, and theutility data 126 are related. In accordance with some embodiments, theaerially collected geographic surface data 118 is an 8-dimension datacube (red, blue, green, infrared, x-coordinate, y-coordinate,z-coordinate, and intensity) for every location within the targetgeographic area 130 scanned by the aerial data acquisition unit 120.

In accordance with the present disclosure, the aerially collectedgeographic surface data 118 can be interpreted by the impervious surfacemapping computing system 100 during the data processing phase 166 torecognize and classify objects, structures, and flora (collectivelyreferred to as ground phenomenon) of the target geographic area 130,including impervious services. As discussed in more detail below, basedon this analysis of the aerially collected geographic surface data 118,the impervious surface mapping computing system 100 can ultimatelydetermine a derived area of impervious surface 140 and associate thesurfaces to particular parcels. Example processes for determining thederived area of impervious surface 140 that can be executed by theimpervious surface mapping computing system 100 are presented in FIGS.2-3.

FIG. 2 shows an example impervious surface mapping process 200 utilizingaerially collected four-band imagery and LIDAR data that can be executedby the impervious surface mapping computing system 100. Referring toFIGS. 1-2, after the process 200 starts at 202, a municipal parameterset 102 is received at 204. The municipal parameter set 102 can include,for example, a plurality of rules 104 associated with impervious surfacedetermination. As is to be appreciated, the rules 104 can vary based onthe municipality being mapped. At 206, parcel data 106 and utility data126 associated with a target geographic area 130 is received. At 208,aerially collected geographic surface data 118 from the targetgeographic area 130 is received by the impervious surface mappingcomputing system 100. As described above, the aerially collectedgeographic surface data 118 can include four-band imagery data and LIDARdata can be co-collected by an aerial data acquisition unit 120 that isflown over the target geographic area 130.

In order to aid in the image analysis, at 210, the impervious surfacemapping computing system 100 can first group the aerially collectedgeographic surface data 118 into a plurality of geographic zones (i.e.,X number of zones). In some embodiments, the impervious surface mappingcomputing system 100 utilizes ten geographic zones, although thisdisclosure is not so limited. The geographic zones can be zones thatgenerally share common characteristics, such as urban, rural, forest,etc., as each zone can be separately processed to classify the groundphenomenon located therein.

The impervious surface mapping computing system 100 can utilize a zonegrouping threshold in order to define X number of zones. While the typeof zone grouping threshold can vary, in some embodiments, the zonegrouping threshold can utilize infrared data obtained from the imagesensor 122. The infrared data can be used to identify a relativepercentage of vegetation, as vegetation provides a readily identifiableinfrared signature. The target geographic area 130 can, for instance,can be automatically grouped into zones by the impervious surfacemapping computing system 100 based on a particular percentage ofvegetation such that each location within the target geographic area 130is assigned to a single zone. A first zone may contain geographic areashaving between 90%-100% vegetation, a second zone may geographic areashaving between 85%-90% of vegetation and so forth. In other embodiments,however, the zone grouping threshold can be based on different data,such as one or more data dimensions collected by the aerial dataacquisition unit 120. By dividing the target geographic area 130 intosmaller geographic zones (i.e., see FIG. 4), the image processingparameters for each zone can be fine-tuned to identify impervioussurfaces within each individual zone with greater specificity andaccuracy.

At 212-222, the process 200 can step through each of the geographiczones (i.e., X number of zones) to analytically classify each surfacelocated within the zone. At 212, a variable N is set to 1. At 214, forthe first zone, object-based image analysis (OBIA) techniques can beused by the impervious surface mapping computing system 100, whichutilizes processes that segment and classify pixels within the imageinto groups (e.g., homogeneous objects). At 216, each of the pluralityof segments can be classified based on the imagery data and LIDAR datacollected from the segment. The classification parameters for the firstzone can be uniquely defined at 218, as the classification parametersfor each of the plurality of zones used by the impervious surfacemapping computing system 100 can vary.

At 220, the impervious surface mapping computing system 100 determinesif it has processed through each of the geographic zones. If it has not,it loops back to 214 after incrementing the variable N at 222. Otherwisethe process proceeds to 224 to determine a derived area of impervioussurface associated with parcels within the target geographic area 130.The derived area of impervious surface can be based on theclassification of all ground phenomena and a quantification of theground phenomena that is an impervious surface. The process ends at 226.

Referring now to FIG. 3, a process 300 is depicted in which theclassification parameters for one or more zone can be updated, such asthrough user intervention, as to increase the accuracy of classificationand proper application of processing rules. At 302, each of theplurality of segments within a particular zone can be initiallyclassified based on the imagery data and LIDAR data collected from thesegment, similar to step 216 in FIG. 2. The classification parametersfor the first zone can be uniquely defined at 304 based on this attempt.At 306, the classifications can be presented to a user for qualitycontrol. The user of the impervious surface mapping computing system 100can investigate the classifications to assess the accuracy. Forinstance, the user can re-classify certain ground phenomenon of aparticular zone by submitting to the impervious surface mappingcomputing system 100 an update to the classifications at 308. If anupdate is received, the process can loop back to 302 and re-classifyeach of the segments based on the update to the classificationparameters. This iterative process can repeat until no further updatesare received. If no updates are received, the process can continue to310 where the unique classification parameters are set for the zone. Theprocess can continue through each of the zones, as indicated by 312 and314, until each of the zones has received one or more updates to theclassifications, as may be needed. At 316, based on the classifications,the derived area of impervious surface associated with parcels withinthe target geographic area 130 can be determined. The process ends at318.

FIG. 4 schematically depicts an example target geographic area 430 beinggrouped into geographic zones 450A-E for determination of uniqueclassification parameters. The target geographic area 430 includes areasof relatively dense population along with rural areas. The targetgeographic area 430 also contains areas having high percentages ofvegetation and areas of lesser percentages of vegetation. Based on theimage processing described herein and indicated by arrow 432, the targetgeographic area 430 can be divided into the geographic zones 450A-E,which each share similar qualities (i.e., similar data signatures basedon the aerially collected geographic surface data). For the processedgeographic area 460 of FIG. 4, a vegetation threshold is used todetermine the geographic zones 450A-E, although this disclosure is notso limited. Further, while five different zones geographic 450A-E aredepicted in FIG. 4, in other embodiments, the image processing canidentify more or fewer different zones. As provided above, uniqueclassification parameters of each of the geographic zones 450A-E can bedetermined by the impervious surface mapping computing system 100 inorder to increase the accuracy of the classification processing.

FIGS. 5-7 schematically depict the derivation of impervious surface areaof a portion of an example target geographic zone. Referring first toFIG. 5, the target geographic area 530 includes a residential areahaving typical features, such as streets 534 and municipal sidewalks546. For illustration purposes, the target geographic area 530 alsoincludes other ground phenomenon, such as dwellings 536, driveways 538,and residential sidewalks 548. Also shown are patios 542 and sheds 544.As is to be appreciated, the target geographic area 530 is to representa portion of the target geographic area 430 (FIG. 4) or the targetgeographic area 130 (FIG. 1).

In accordance with the present disclosure, an aerial data acquisitionunit can be flown over the target geographic area 530 to collectgeographic surface data. An impervious surface mapping computing systemsimilar to the impervious surface mapping computing system 100 can beused to process the aerially collected geographic surface data inaccordance with the present disclosure. FIG. 6 schematically depicts theprocessed geographic area 560. As shown, parcel lines 506, as receivedby the impervious surface mapping computing system, have been overlaidto delineate the property boundaries of parcels within the processedgeographic area 560. Areas of solid black in FIG. 6 represent impervioussurfaces, as derived by an impervious surface mapping computing systemand based on the municipality's rules for determining the same. As shownin FIG. 5, areas of impervious surface identified by the impervioussurface mapping computing system include the dwellings 536, patios 542,residential sidewalks 548, and sheds 544. These surfaces were identifiedin accordance with the image analysis techniques described above. Morespecifically, each of these objects was classified based on their reddata, blue data, green data, infrared data, x-coordinate, y-coordinate,z-coordinate, and intensity data. Notably, the streets 534 are excludedfrom the derived area of impervious surface in FIG. 6, as in thisembodiment those impervious surfaces are not to be included in theimpervious surface determination.

Rules associated with quantifying the impervious surfaces of the targetgeographic area 530 can determine how certain surfaces are to be handledby the impervious surface mapping computing system. For the purposes ofillustration, the parcel lines 506 of FIG. 6 are shown to separate themunicipal sidewalks 546 into two areas. A first area 572 is shown to bewithin an associated parcel and a second area 574 is shown to be outsidethe associated parcel. As shown, a driveway apron 576 associated with aparcel is also shown to be outside parcel boundary defined by the parcelline 506. Municipalities can decide how such occurrence should behandled by way of rules provided to the impervious surface mappingcomputing system (i.e., rules 104 in FIG. 1). Example Rules A-C areschematically illustrated which show example approaches to handling theimpervious surface associated with the municipal sidewalk 546 and thedriveway apron 576.

Referring first to Rule A, as shown in FIG. 6, the first area 572 of themunicipal sidewalks 546 is identified as an impervious surfaceattributable to the associated parcel. The driveway apron 576, however,is not. Referring next to Rule B, neither the driveway apron 576 nor thefirst area 572 are identified as impervious surfaces attributable to theassociated parcel. Finally, as schematically shown, Rule C includes theportion of the driveway 570 that extends to the parcel line 506. It isto be appreciated, that Rules A-C are depicted for illustration purposesonly, as a variety of different processing rules can be implemented byimpervious surface mapping computing systems in accordance with thepresent disclosure. For instance, a rule can establish that the portionof the driveway extending outward from the properly line until it meetsa roadway is to be an impervious surface attributable to the associatedparcel.

As shown in FIG. 7, when the aerially collected geographic surface datais obtained, certain surfaces may be blocked or obscured. For instance,shadows may prohibit adequate data from certain ground phenomenon beingprovided to an aerial data acquisition unit. Additionally, other areasmay be obscured by vegetation, trees, or other objects, generally shownas obstructions 580 in FIG. 7, which can be exacerbated by an increasedangle between the ground phenomenon and the aerial data acquisition unitduring data collection. In accordance with the system and methodsdescribed herein, obscured areas can be contextually quantified based oninformation obtained from surrounding areas and the classificationsthereof. By way of a non-limiting example, FIG. 7 illustrates an exampleobstruction 580 that is a tree overhanging a driveway 570. The tree mayinhibit the ability to obtain surface data on the portion of thedriveway 570 that is located below the tree's foliage. Moreparticularly, neither an image sensor nor a LIDAR sensor of an aerialdata acquisition unit may be able to obtain a suitable amount of datapoints for that particular area during a data collection phase.

As shown by driveway 570A, the portion of the driveway obscured by thetree is initially not identified as an impervious surface, as little orno data was collected from that portion. However, with an impervioussurface mapping computing system identifying the surrounding area as adriveway, the system can be aware that driveways are typically ofgenerally rectangular shape. As shown, the impervious surface mappingcomputing system can extend a phantom boundary 584 between points 582 toform driveway 570B into a rectangle. Once formed, the area of thedriveway originally obscured in the aerially collected data can beidentified as part of the driveway 570C and counted toward the amount ofimpervious surface for a parcel.

While FIG. 7 schematically depicts an example of a tree obscuring, it isto be appreciated that for any target geographic area, a number ofobjects may be obscured by shadow, voids, and the like. The imageprocessing of the impervious surface mapping computing system describedherein can contextually determine a surface type of those obscuredareas.

Referring now to FIG. 8, an example correlation of parcel-specificimpervious surface area to a utility customer number is depicted. Asshown, each parcel in the processed geographic area 560 is associatedwith a parcel identification number (shown as PID1-PID6). Such parcelidentification number can be associated, for instance, with the propertytax paid for the parcel. This parcel data can be provided to theimpervious surface mapping computing system as parcel data 106, similarthe parcel data 106 illustrated in FIG. 1. Reporting 590 includes alisting 592 of parcel identification numbers. Also utilized by thereporting 590 is a utility customer number 594, which can be associatedwith an address. Using a geocoding process, an impervious surfacemapping computing system can correlate the address associated with theutility customer number 594 with the parcel identification number 592.Then using the processing rules for the municipality, the square footage596 of impervious surface attributable to each parcel can be provided.The square footage can be used to determine a billing amount, which canbe based on equivalent residential units (ERUs), for instance. In somemunicipalities, for example, one ERU is equal to 2,536 square feet.Based on the square footage 596 in the reporting 590, the monthlyutility fee based on ERUs can be calculated.

In general, it will be apparent to one of ordinary skill in the art thatat least some of the embodiments described herein can be implemented inmany different embodiments of software, firmware, and/or hardware. Thesoftware and firmware code can be executed by a processor or any othersimilar computing device. The software code or specialized controlhardware that can be used to implement embodiments is not limiting. Forexample, embodiments described herein can be implemented in computersoftware using any suitable computer software language type, such as,for example, conventional or object-oriented techniques. Such softwarecan be stored on any type of suitable computer-readable medium or media,such as, for example, a magnetic or optical storage medium. Theoperation and behavior of the embodiments can be described withoutspecific reference to specific software code or specialized hardwarecomponents. The absence of such specific references is feasible, becauseit is clearly understood that artisans of ordinary skill would be ableto design software and control hardware to implement the embodimentsbased on the present description with no more than reasonable effort andwithout undue experimentation.

Moreover, the processes described herein can be executed by programmableequipment, such as computers or computer systems and/or processors.Software that can cause programmable equipment to execute processes canbe stored in any storage device, such as, for example, a computer system(nonvolatile) memory, an optical disk, magnetic tape, or magnetic disk.Furthermore, at least some of the processes can be programmed when thecomputer system is manufactured or stored on various types ofcomputer-readable media.

It can also be appreciated that certain portions of the processesdescribed herein can be performed using instructions stored on acomputer-readable medium or media that direct a computer system toperform the process steps. A computer-readable medium can include, forexample, memory devices such as diskettes, compact discs (CDs), digitalversatile discs (DVDs), optical disk drives, or hard disk drives. Acomputer-readable medium can also include memory storage that isphysical, virtual, permanent, temporary, semipermanent, and/orsemitemporary.

A “computer,” “computer system,” “host,” “server,” or “processor” canbe, for example and without limitation, a processor, microcomputer,minicomputer, server, mainframe, laptop, personal data assistant (PDA),wireless e-mail device, cellular phone, pager, processor, fax machine,scanner, or any other programmable device configured to transmit and/orreceive data over a network. Computer systems and computer-based devicesdisclosed herein can include memory for storing certain software modulesused in obtaining, processing, and communicating information. It can beappreciated that such memory can be internal or external with respect tooperation of the disclosed embodiments. The memory can also include anymeans for storing software, including a hard disk, an optical disk,floppy disk, ROM (read only memory), RAM (random access memory), PROM(programmable ROM), EEPROM (electrically erasable PROM) and/or othercomputer-readable media. Non-transitory computer-readable media, as usedherein, comprises all computer-readable media except for transitory,propagating signals.

In various embodiments disclosed herein, a single component can bereplaced by multiple components and multiple components can be replacedby a single component to perform a given function or functions. Exceptwhere such substitution would not be operative, such substitution iswithin the intended scope of the embodiments. The computer systems cancomprise one or more processors in communication with memory (e.g., RAMor ROM) via one or more data buses. The data buses can carry electricalsignals between the processor(s) and the memory. The processor and thememory can comprise electrical circuits that conduct electrical current.Charge states of various components of the circuits, such as solid statetransistors of the processor(s) and/or memory circuit(s), can changeduring operation of the circuits.

The foregoing description of embodiments and examples has been presentedfor purposes of illustration and description. It is not intended to beexhaustive or limiting to the forms described. Numerous modificationsare possible in light of the above teachings. Some of thosemodifications have been discussed, and others will be understood bythose skilled in the art. The embodiments were chosen and described inorder to best illustrate principles of various embodiments as are suitedto particular uses contemplated. The scope is, of course, not limited tothe examples set forth herein, but can be employed in any number ofapplications and equivalent devices by those of ordinary skill in theart. Rather it is hereby intended the scope of the invention to bedefined by the claims appended hereto.

1. A method of identifying impervious surfaces based on aeriallycollected geographic surface data, the method comprising: receiving, byan impervious surface mapping computing system, parcel data for a targetgeographic area, wherein the target geographic area comprises aplurality of parcels and the parcel data comprises parcel boundary datafor each of the plurality of parcels; receiving, by the impervioussurface mapping computing system, utility data for the target geographicarea, wherein the utility data comprises a plurality of customeridentifiers that are each associated with an associated propertyaddress; receiving, by the impervious surface mapping computing system,aerially collected geographic surface data from the target geographicarea, wherein the aerially collected geographic surface data comprisesimagery data and light detection and ranging (LIDAR) data of groundphenomenon for each of the plurality of parcels; based on one or morerules for identifying impervious surfaces for the target geographicarea, determining, by the impervious surface mapping computing system, aderived area of impervious surface associated with each of the pluralityof parcels within the target geographic area; and based on the derivedarea of impervious surface associated with each of the plurality ofparcels, determining a utility billing amount for each of the pluralityof customer identifiers.
 2. The computer-based method of claim 1,wherein the aerially collected geographic surface data comprises aplurality of pixels.
 3. The computer-based method of claim 2, furthercomprising: segmenting, by the impervious surface mapping computingsystem, adjoining pixels of the aerially collected geographic surfacedata into a plurality of segments; and classifying, by the impervioussurface mapping computing system, each of the plurality of segments. 4.The computer-based method of claim 2, wherein determining the derivedarea of impervious surface associated with each of the plurality ofparcels within the target geographic area is based on a surface typeidentified within the parcel, wherein the surface type is based on theclassification of each of the plurality of segments.
 5. Thecomputer-based method of claim 4, further comprising: receiving, by theimpervious surface mapping computing system, a municipal parameter set,wherein the municipal parameter set identifies one or more rules foridentifying impervious surfaces for the target geographic area.
 6. Thecomputer-based method of claim 1, further comprising: based on a zonegrouping threshold associated with the aerially collected geographicsurface data, grouping the aerially collected geographic surface datainto a plurality of geographic zones.
 7. The computer-based method ofclaim 6, wherein the grouping of the aerially collected geographicsurface data into the plurality of geographic zones is based on avegetation index.
 8. The computer-based method of claim 1, wherein theimagery data comprises four-band imagery.
 9. The computer-based methodof claim 1, wherein the derived area of impervious surface comprises atleast one area obscured in the aerially collected geographic surfacedata, wherein the at least one area obscured in the aerially collectedgeographic surface data is obscured by any of a tree and a shadow. 10.The computer-based method of claim 9, further comprising: quantifying,by the impervious surface mapping computing system, the at least onearea obscured in the aerially collected geographic surface data based oninformation obtained from a surrounding area.
 11. The computer-basedmethod of claim 1, wherein the derived area of impervious surfaceassociated with at least one of the plurality of parcels includes anarea of impervious surface that extends beyond a property line of the atleast one of the plurality of parcels.
 12. The computer-based method ofclaim 11, wherein the area of impervious surface that extends beyond theproperty line of the at least one of the parcels is bounded by a roadedge.
 13. The computer-based method of claim 1, wherein the derived areaof impervious surface comprises a plurality of types of impervioussurfaces.
 14. A method of identifying impervious surfaces based onaerially collected geographic surface data, the method comprising:receiving, by an impervious surface mapping computing system, amunicipal parameter set, wherein the municipal parameter set identifiesone or more rules for identifying impervious surfaces for a targetgeographic area; receiving, by the impervious surface mapping computingsystem, parcel data for the target geographic area, wherein the targetgeographic area comprises a plurality of parcels; receiving, by theimpervious surface mapping computing system, utility data for the targetgeographic area, wherein the utility data identifies a plurality ofutility customers; receiving, by the impervious surface mappingcomputing system, aerially collected geographic surface data from thetarget geographic area, wherein the aerially collected geographicsurface data comprises data associated ground phenomenon for each of theplurality of parcels; based on the one or more rules for identifyingimpervious surfaces for the target geographic area, determining, by theimpervious surface mapping computing system, a derived area ofimpervious surface associated with each of the plurality of parcelswithin the target geographic area; and associating the derived area ofimpervious surface associated with each of the plurality of parcels witha respective one of the plurality of utility customers.
 15. Thecomputer-based method of claim 14, further comprising: determining autility billing amount for each plurality of utility customers, whereinthe utility billing amount is based on equivalent residential units(ERUs).
 16. The computer-based method of claim 15, further comprising:generating, by the impervious surface mapping computing system, a parcelmapping for each of the plurality of parcels that schematically showsthe surfaces on each respective parcel identified as impervioussurfaces.
 17. A method of identifying impervious surfaces based onaerially collected geographic surface data, the method comprising:receiving, by an impervious surface mapping computing system, one ormore rules for identifying impervious surfaces for a target geographicarea; receiving, by the impervious surface mapping computing system,parcel data for the target geographic area, wherein the targetgeographic area comprises a plurality of parcels and the parcel datacomprises parcel boundary data for each of the plurality of parcels;receiving, by the impervious surface mapping computing system, utilitydata for the target geographic area, wherein the utility data comprisesa plurality of customer identifiers that are each associated with anassociated property address; associating, by the impervious surfacemapping computing system, each of the plurality of customer identifierswith a respective one of the plurality of parcels; receiving, by theimpervious surface mapping computing system, aerially collectedgeographic surface data from the target geographic area; based on theone or more rules for identifying impervious surfaces for the targetgeographic area, determining, by the impervious surface mappingcomputing system, a derived area of impervious surface associated witheach of the plurality of parcels within the target geographic area;determining, by the impervious surface mapping computing system, autility billing amount for each of the plurality of customer identifiersbased on the derived area of impervious surface associated with each ofthe plurality of parcels.
 18. The computer-based method of claim 17,wherein the aerially collected geographic surface data comprises aplurality of pixels.
 19. The computer-based method of claim 18, furthercomprising: segmenting, by the impervious surface mapping computingsystem, adjoining pixels of the aerially collected geographic surfacedata into a plurality of segments; and classifying, by the impervioussurface mapping computing system, each of the plurality of segments. 20.The computer-based method of claim 19, wherein the aerially collectedgeographic surface data from the target geographic area comprisesimagery data and light detection and ranging (LIDAR) data of groundphenomenon for each of the plurality of parcels.